A method for analysis of volatile organic compounds (VOCs) from microbial cultures was established using proton transfer reaction-mass spectrometry (PTR-MS). A newly developed sampling system was coupled to a PTR-MS instrument to allow on-line monitoring of VOCs in the dynamic headspaces of microbial cultures. The novel PTR-MS method was evaluated for four reference organisms: Escherichia coli, Shigella flexneri, Salmonella enterica, and Candida tropicalis. Headspace VOCs in sampling bottles containing actively growing cultures and uninoculated culture medium controls were sequentially analyzed by PTR-MS. Characteristic marker ions were found for certain microbial cultures: C. tropicalis could be identified by several unique markers compared with the other three organisms, and E. coli and S. enterica were distinguishable from each other and from S. flexneri by specific marker ions, demonstrating the potential of this method to differentiate between even closely related microorganisms. Although the temporal profiles of some VOCs were similar to the growth dynamics of the microbial cultures, most VOCs showed a different temporal profile, characterized by constant or decreasing VOC levels or by single or multiple peaks over 24 h of incubation. These findings strongly indicate that the temporal evolution of VOC emissions during growth must be considered if characterization or differentiation based on microbial VOC emissions is attempted. Our study may help to establish the analysis of VOCs by on-line PTR-MS as a routine method in microbiology and as a tool for monitoring environmental and biotechnological processes.

No abstract

No abstract

The potential of proton transfer reaction mass spectrometry (PTR-MS) as a tool for classification of milk fats was evaluated in relation to quality and authentication issues. Butters and butter oils were subjected to heat and off-flavouring treatments in order to create sensorially defective samples. The effect of the treatments was evaluated by means of PTR-MS analysis, sensory analysis and classical chemical analysis. Subsequently, partial least square-discriminant analysis models (PLS-DA) were fitted to predict the matrix (butter/butter oil) and the sensory grades of the samples from their PTR-MS data. Using a 10-fold cross-validation scheme, 84% of the samples were successfully classified into butter and butter oil classes. Regarding sensory quality, 89% of the samples were correctly classified. As the milk fats were fairly successfully classified by the combination of PTR-MS and PLS-DA, this combination seems a promising approach with potential applications in quality control and control of regulations.